The study of fluid flow turbulence has been an active area of research
for over 100 years, mainly because of its technological importance to
a vast number of applications.
In recent times with the advent of supercomputers and new experimental
imaging techniques, terabyte scale data sets are being generated,
and hence storage as well as analysis of these data has become a major issue.
In this talk a new approach to tackling these data-sets will be described
which relies on selective data storage based on real-time feature extraction
and utilizing data mining tools to aid in the discovery and analysis
of the data. Visualization results will be presented which highlight the type
and number of spatially and temporally evolving coherent features that can
be extracted as well as other high level features. Results will also be
presented where graphs have been used to model the underlying
data set which are then searched for frequently occurring topological
and geometric patterns. General discussion will be given
on the challenges and issues which need to be addressed by the data mining
tools.
Bio:
Ivan Marusic is a McKnight Land-Grant Assistant Professor in the Department
Aerospace Engineering and Mechanics at the University of Minnesota, which
he joined in 1998. Prior to Minnesota, he was a Research Fellow
at the University of Melbourne, Australia, from which he also obtained his
B.E. and Ph.D. degrees. Dr. Marusic is a recipient of a NSF Career Award (2000)
and Packard Fellowship of Engineering and Science (2001).
His primarily research is in the area of experimental and theoretical
fluid mechanics with an emphasis on turbulent shear flows. The complexity
and large size of turbulence data sets has lead him to investigate novel
analysis tools based on visualization and data mining. This work is in
collaboration with colleagues in the Department of Computer Science at the
University of Minnesota.
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